The development of validated algorithms for automated handling of artifacts is essential for
reliable and fast processing of EEG signals. Recently, there have been methodological …

Effective management of EEG artifacts is pivotal for accurate neurological diagnostics,
particularly in detecting early stages of Alzheimer's disease. This review delves into the …

Electroencephalogram (EEG) is widely utilized since it provides information on brain activity
without causing any harm to the subject. EEG's great sensitivity makes it vulnerable to …

Machine learning techniques such as deep learning have been increasingly used to assist
EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In …

Electroencephalography (EEG) artifacts are very common in clinical diagnosis and can
heavily impact diagnosis. Manual screening of artifact events is labor-intensive with little …

The electroencephalogram (EEG) recordings from the human brain are useful for detecting
various brain syndromes. These recordings are typically contaminated by high amplitude …

Electroencephalogram (EEG) signals are mostly contaminated with ocular artifacts (OAs)
due to eye movements and eye blinks. These artifacts make the EEG recordings difficult to …

Electrooculography is a technique that detects and analyses eye movement based on
electrical potentials recorded using electrodes placed around the eyes. The electrical signal …

While Magnetoencephalography (MEG) and electroencephalography (EEG) are well-known
neuroimaging techniques to capture a myriad of brain activities and stimulations, accessing …

Due to the increased usage of digital devices in daily life, particularly among children,
symptoms such as drying of the eyes, eye strain, headaches, blurred vision, etc., have …